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com.microsoft.azure.synapse.ml.causal

OrthoForestDMLParams

trait OrthoForestDMLParams extends DoubleMLParams with HasNumTrees with HasMaxDepth with HasMinSampleLeaf with HasOutputCol

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Inherited
  1. OrthoForestDMLParams
  2. HasOutputCol
  3. HasMinSampleLeaf
  4. HasMaxDepth
  5. HasNumTrees
  6. DoubleMLParams
  7. HasParallelismInjected
  8. HasParallelism
  9. HasWeightCol
  10. HasMaxIter
  11. HasFeaturesCol
  12. HasOutcomeCol
  13. HasTreatmentCol
  14. Params
  15. Serializable
  16. Serializable
  17. Identifiable
  18. AnyRef
  19. Any
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Visibility
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Abstract Value Members

  1. abstract def copy(extra: ParamMap): Params
    Definition Classes
    Params
  2. abstract val uid: String
    Definition Classes
    Identifiable

Concrete Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T
    Attributes
    protected
    Definition Classes
    Params
  4. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. def awaitFutures[T](futures: Array[Future[T]]): Seq[T]
    Attributes
    protected
    Definition Classes
    HasParallelismInjected
  7. final def clear(param: Param[_]): OrthoForestDMLParams.this.type
    Definition Classes
    Params
  8. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  9. val confidenceLevel: DoubleParam
    Definition Classes
    DoubleMLParams
  10. val confounderVecCol: Param[String]
  11. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  12. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  13. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  14. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  15. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  16. def explainParams(): String
    Definition Classes
    Params
  17. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  18. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  19. val featuresCol: Param[String]

    The name of the features column

    The name of the features column

    Definition Classes
    HasFeaturesCol
  20. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  21. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  22. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  23. def getConfidenceLevel: Double
    Definition Classes
    DoubleMLParams
  24. def getConfounderVecCol: String
  25. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  26. def getExecutionContextProxy: ExecutionContext
    Definition Classes
    HasParallelismInjected
  27. def getFeaturesCol: String

    Definition Classes
    HasFeaturesCol
  28. def getHeterogeneityVecCol: String
  29. def getMaxDepth: Int
    Definition Classes
    HasMaxDepth
  30. final def getMaxIter: Int
    Definition Classes
    HasMaxIter
  31. def getMinSamplesLeaf: Int
    Definition Classes
    HasMinSampleLeaf
  32. def getNumTrees: Int
    Definition Classes
    HasNumTrees
  33. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  34. def getOutcomeCol: String
    Definition Classes
    HasOutcomeCol
  35. def getOutcomeModel: Estimator[_ <: Model[_]]
    Definition Classes
    DoubleMLParams
  36. def getOutcomeResidualCol: String
  37. def getOutputCol: String

    Definition Classes
    HasOutputCol
  38. def getOutputHighCol: String
  39. def getOutputLowCol: String
  40. def getParallelism: Int
    Definition Classes
    HasParallelism
  41. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  42. def getSampleSplitRatio: Array[Double]
    Definition Classes
    DoubleMLParams
  43. def getTreatmentCol: String
    Definition Classes
    HasTreatmentCol
  44. def getTreatmentModel: Estimator[_ <: Model[_]]
    Definition Classes
    DoubleMLParams
  45. def getTreatmentResidualCol: String
  46. def getWeightCol: String

    Definition Classes
    HasWeightCol
  47. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  48. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  49. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  50. val heterogeneityVecCol: Param[String]
  51. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  52. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  53. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  54. val maxDepth: IntParam
    Definition Classes
    HasMaxDepth
  55. final val maxIter: IntParam
    Definition Classes
    HasMaxIter
  56. val minSamplesLeaf: IntParam
    Definition Classes
    HasMinSampleLeaf
  57. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  58. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  59. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  60. val numTrees: IntParam
    Definition Classes
    HasNumTrees
  61. val outcomeCol: Param[String]
    Definition Classes
    HasOutcomeCol
  62. val outcomeModel: EstimatorParam
    Definition Classes
    DoubleMLParams
  63. val outcomeResidualCol: Param[String]
  64. val outputCol: Param[String]

    The name of the output column

    The name of the output column

    Definition Classes
    HasOutputCol
  65. val outputHighCol: Param[String]
  66. val outputLowCol: Param[String]
  67. val parallelism: IntParam
    Definition Classes
    HasParallelism
  68. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  69. val sampleSplitRatio: DoubleArrayParam
    Definition Classes
    DoubleMLParams
  70. final def set(paramPair: ParamPair[_]): OrthoForestDMLParams.this.type
    Attributes
    protected
    Definition Classes
    Params
  71. final def set(param: String, value: Any): OrthoForestDMLParams.this.type
    Attributes
    protected
    Definition Classes
    Params
  72. final def set[T](param: Param[T], value: T): OrthoForestDMLParams.this.type
    Definition Classes
    Params
  73. def setConfidenceLevel(value: Double): OrthoForestDMLParams.this.type

    Set the higher bound percentile of ATE distribution.

    Set the higher bound percentile of ATE distribution. Default is 0.975. lower bound value will be automatically calculated as 100*(1-confidenceLevel) That means by default we compute 95% confidence interval, it is [2.5%, 97.5%] percentile of ATE distribution

    Definition Classes
    DoubleMLParams
  74. def setConfounderVecCol(value: String): OrthoForestDMLParams.this.type

    Set confounder vector column

  75. final def setDefault(paramPairs: ParamPair[_]*): OrthoForestDMLParams.this.type
    Attributes
    protected
    Definition Classes
    Params
  76. final def setDefault[T](param: Param[T], value: T): OrthoForestDMLParams.this.type
    Attributes
    protected[org.apache.spark.ml]
    Definition Classes
    Params
  77. def setFeaturesCol(value: String): OrthoForestDMLParams.this.type

    Definition Classes
    HasFeaturesCol
  78. def setHeterogeneityVecCol(value: String): OrthoForestDMLParams.this.type

    Set heterogeneity vector column

  79. def setMaxDepth(value: Int): OrthoForestDMLParams.this.type

    Set max depth of the trees to be used in the forest

    Set max depth of the trees to be used in the forest

    Definition Classes
    HasMaxDepth
  80. def setMaxIter(value: Int): OrthoForestDMLParams.this.type

    Set the maximum number of confidence interval bootstrapping iterations.

    Set the maximum number of confidence interval bootstrapping iterations. Default is 1, which means it does not calculate confidence interval. To get Ci values please set a meaningful value

    Definition Classes
    DoubleMLParams
  81. def setMinSamplesLeaf(value: Int): OrthoForestDMLParams.this.type

    Set number of samples in the leaf node of trees to be used in the forest

    Set number of samples in the leaf node of trees to be used in the forest

    Definition Classes
    HasMinSampleLeaf
  82. def setNumTrees(value: Int): OrthoForestDMLParams.this.type

    Set number of trees to be used in the forest

    Set number of trees to be used in the forest

    Definition Classes
    HasNumTrees
  83. def setOutcomeCol(value: String): OrthoForestDMLParams.this.type

    Set name of the column which will be used as outcome

    Set name of the column which will be used as outcome

    Definition Classes
    HasOutcomeCol
  84. def setOutcomeModel(value: Estimator[_ <: Model[_]]): OrthoForestDMLParams.this.type

    Set outcome model, it could be any model derived from 'org.apache.spark.ml.regression.Regressor' or 'org.apache.spark.ml.classification.ProbabilisticClassifier'

    Set outcome model, it could be any model derived from 'org.apache.spark.ml.regression.Regressor' or 'org.apache.spark.ml.classification.ProbabilisticClassifier'

    Definition Classes
    DoubleMLParams
  85. def setOutcomeResidualCol(value: String): OrthoForestDMLParams.this.type

    Set outcome residual column

  86. def setOutputCol(value: String): OrthoForestDMLParams.this.type

    Definition Classes
    HasOutputCol
  87. def setOutputHighCol(value: String): OrthoForestDMLParams.this.type

    Set output column for effect upper bound

  88. def setOutputLowCol(value: String): OrthoForestDMLParams.this.type

    Set output column for effect lower bound

  89. def setParallelism(value: Int): OrthoForestDMLParams.this.type
    Definition Classes
    DoubleMLParams
  90. def setSampleSplitRatio(value: Array[Double]): OrthoForestDMLParams.this.type

    Set the sample split ratio, default is Array(0.5, 0.5)

    Set the sample split ratio, default is Array(0.5, 0.5)

    Definition Classes
    DoubleMLParams
  91. def setTreatmentCol(value: String): OrthoForestDMLParams.this.type

    Set name of the column which will be used as treatment

    Set name of the column which will be used as treatment

    Definition Classes
    HasTreatmentCol
  92. def setTreatmentModel(value: Estimator[_ <: Model[_]]): OrthoForestDMLParams.this.type

    Set treatment model, it could be any model derived from 'org.apache.spark.ml.regression.Regressor' or 'org.apache.spark.ml.classification.ProbabilisticClassifier'

    Set treatment model, it could be any model derived from 'org.apache.spark.ml.regression.Regressor' or 'org.apache.spark.ml.classification.ProbabilisticClassifier'

    Definition Classes
    DoubleMLParams
  93. def setTreatmentResidualCol(value: String): OrthoForestDMLParams.this.type

    Set treatment residual column

  94. def setWeightCol(value: String): OrthoForestDMLParams.this.type

    Definition Classes
    HasWeightCol
  95. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  96. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  97. val treatmentCol: Param[String]
    Definition Classes
    HasTreatmentCol
  98. val treatmentModel: EstimatorParam
    Definition Classes
    DoubleMLParams
  99. val treatmentResidualCol: Param[String]
  100. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  101. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  102. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  103. val weightCol: Param[String]

    The name of the weight column

    The name of the weight column

    Definition Classes
    HasWeightCol

Inherited from HasOutputCol

Inherited from HasMinSampleLeaf

Inherited from HasMaxDepth

Inherited from HasNumTrees

Inherited from DoubleMLParams

Inherited from HasParallelismInjected

Inherited from HasParallelism

Inherited from HasWeightCol

Inherited from HasMaxIter

Inherited from HasFeaturesCol

Inherited from HasOutcomeCol

Inherited from HasTreatmentCol

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

getParam

param

setParam

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